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The DEFACTO System: Training Incident Commanders

The DEFACTO System: Training Incident Commanders. Nathan Schurr Janusz Marecki, Milind Tambe, Nikhil Kasinadhuni, and J. P. Lewis University of Southern California Paul Scerri Carnegie Mellon University. Outline. Motivation and Domain DEFACTO Team Level Adjustable Autonomy

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The DEFACTO System: Training Incident Commanders

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  1. The DEFACTO System:Training Incident Commanders Nathan Schurr Janusz Marecki, Milind Tambe, Nikhil Kasinadhuni, and J. P. Lewis University of Southern California Paul Scerri Carnegie Mellon University

  2. Outline • Motivation and Domain • DEFACTO • Team Level Adjustable Autonomy • Experiments with DEFACTO • Conclusions

  3. Motivation: Help Incident Commanders • Incident Commander • First Response • Disaster Rescue Scenario • Urban Environment • Large Scale • Crime Scene • Incident commander must control situation, monitor situation, and allocate resources • Goal: Initially a Training Simulation • Later: Decision Support/Replacement

  4. LAFD Exercise: Simulations by People Playing Roles

  5. Aims of DEFACTO • LAFD Exercise Challenges • Personnel Heavy • Smaller Scale • Low Fidelity Environment • Key Exercise Components • Communication • Allocation • Agent-teams replace people playing roles • Demonstrating Effective Flexible Agent Coordination of Teams via Omnipresence

  6. Outline • Motivation and Domain • DEFACTO • Team Level Adjustable Autonomy • Experiments with DEFACTO • Conclusions

  7. DEFACTO Architecture

  8. Disaster Rescue Simulation:USC Map, Different underlying simulators Statistics

  9. Challenges in Extending to Human-Agent Teams • Teamwork • Communication • Role Allocation • Agent team to incorporate human • Adjustable Autonomy (Scerri et al JAIR 2002) • Interface

  10. DEFACTO • Teamwork Proxies • Machinetta • Continued development with CMU • Used in many other domains – UAVs, sensor nets etc. • Flexible Interaction • Team Level Adjustable Autonomy Strategies • Dynamic Strategy Selection • Omni-Viewer • 2D – Standard with Simulator • 3D – Developed by us • Interaction

  11. DEFACTO Architecture

  12. Proxy Architecture • Abstracted Theories of Teamwork (Scerri et al AAMAS 03) • Communication: communication with other proxies • Coordination: reasoning about team plans and communication • State: the working memory of the proxy • Adjustable Autonomy: reasoning about whether to act autonomously or pass control to the team member • RAP Interface: communication with the team member Other RAP Communication RAP Interface Proxies State Coordination Adjustable Autonomy

  13. Teamwork Proxies • Higher level TOP • Reuse across domain • Flexible Teamwork (Tambe JAIR 97) • Communication • Joint Intentions (Cohen & Levesque 1991) • Allocation • Role allocation algorithms (Xu et al AAMAS 2005) • Machinetta • Platform Independent • Modular Structure • Downloadable – Free, Publicly available

  14. Outline • Motivation and Domain • DEFACTO • Team Level Adjustable Autonomy • Experiments with DEFACTO • Conclusions

  15. Adjustable Autonomy(AA) Strategies for Teams • Agents dynamically adjust own level of autonomy • Agents act autonomously, but also... • Give up autonomy, transferring control to humans • When to transfer decision-making control • Whenever human has superior expertise • Yet, too many interrupts also problematic • Previous: Individual agent-human interaction

  16. AA: Novel Challenges in Teams • Transfer of control strategies for AA in teams • Planned sequence of transfers of control • AT - Team level A strategy • H - Human strategy for all tasks • AH - Individual A followed by H • ATH - Team level A strategy followed by H • Goal: Improve Team Performance

  17. DEFACTO Architecture

  18. Omni-Viewer

  19. DEFACTO Movie

  20. Outline • Motivation and Domain • DEFACTO • Team Level Adjustable Autonomy • Experiments with DEFACTO • Conclusions

  21. Experiments • Initial evaluation of system and of strategies • Details • 3 Subjects • Allocation Viewer • Same Map for each scenario • Building size and location • Initial position of fires • 4, 6, and 10 agents • A, H, AH, ATH Strategies • Averaged over 3 runs

  22. Empirical Studies with Users

  23. Conclusions from Results • No strategy dominates through all cases • Humans may sometimes degrade agent team results • Slope of strategy A > Slope of H • Humans are not as good at exploiting additional agents resources • If EQH is low, then as we grow to larger numbers of agents, A will dominate AH, ATH and H • Dip at 6… • LAFD – “Not surprising.”

  24. Summary • DEFACTO • Teamwork • Team Level Adjustable Autonomy Strategies • Interface • Experimented with strategies for adjustable autonomy • Future Directions • Experiments with LAFD • Study strategy behavior • Train the “system” • Training today, real response in the future.

  25. Future

  26. Thank You • Email: schurr@usc.edu • Web Site: http://teamcore.usc.edu • Machinetta • http://teamcore.usc.edu/doc/Machinetta/ • Thanks • CREATE Center • Fred Pighin and Pratik Patil

  27. Related Work: Disaster Response Simulations • LA County Fire Department Simulators • DEFACTO focuses on “incident commander” • “Environment” simulators: • E.g., Terrasim, EPICS • Not provide on agent behaviors • “Agent-based” simulators • E.g., Battlefield simulators • Adjustable autonomy

  28. Strategy Models

  29. Models of the Strategies

  30. Models of the Strategies

  31. Outline • Motivation • Objectives • CREATE Research Center • Current State of the Art • DEFACTO • Simulator • Teamwork Proxies • 3D Visualization • Team Level Adjustable Autonomy • Models • Predictions • Experiments with DEFACTO • Conclusions

  32. DEFACTO: Key Research Areas • Enable effective interactions of agents with humans • Research: Adjustable autonomy • Previous work: Often single agent-single human interactions • Scale-up to 100s of agents with fire engines, ambulances, police • Research: Scale-up in team coordination • Previous work: Limited numbers of agents coordinating in teams • Visualization • Robust 3D visualization

  33. Adjustable Autonomy:Novel Challenges in Teams • Previous transfer-of-control fails in teams: • Ignore costs to team (just concerned about individual) • One shot transfers of control, too rigid • Transfer control to a human (H) or agent (A) • If human fails to make a decision, miscoordination!! • Forcing agent to decide can cause a poor decision • Expensive lesson learned in the “Electric-Elves” project • Major errors by software assistants • Hence need more flexible transfer of control

  34. Predictions • EQh: Expected quality of human decision • AGH: How many agents human can control • A Strategy has constant slope

  35. CREATE Research Center • Center for Risk and Economic Analysis of Terrorism Events • MANPAD Scenario • Large Scale Disaster • Limited Resources • First Response • Help incident commander control situation • Large Scale • Crime Scene

  36. Simulator • Robocup Rescue • 10 different Simulators • Multiple Agent Types

  37. Team Level AA Model • How to select the strategy among many? • Key idea: Calculate expected utility of different strategies • Mathematical model of strategies • EQ: Quality of an entity’s decision • P: Probability of response of that entity • W: Cost of miscoordination • Traditional Expected Utility • Probability of response * decision quality • Integrate over time

  38. Agents Per Fire Subject A Subject B Subject C

  39. Fire starts on 1st floor Spreads to Attic LA City Fire Dept Exercise: Fire Progression

  40. LAFD officials simulate fire progression and the resource availability Battalion Chief allocates available resources to tasks LAFD Exercise: Simulations by People Playing Roles

  41. Proxy Architecture • Abstracted Theories of Teamwork (Machinetta) • Platform Independent • Modular Structure Other RAP Communication RAP Interface Proxies State Coordination Adjustable Autonomy

  42. DEFACTO Movie

  43. Objectives: Agent-based Simulation Tools for Disaster Response • Improve training and decision making • Present • Teach and evaluate LAFD response tactics • Future • Agent/Robot disaster response • Key research questions in: • Multiagent coordination, Adjustable Autonomy • Visualization of multiagent systems

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